{"id":"W4365134769","doi":"10.1002/gepi.22526","title":"RoPE: A robust profile likelihood method for differential gene expression analysis","year":2023,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; Public Health Ontario; University of Toronto","funders":"Hospital for Sick Children; Government of Canada; Government of Ontario; Natural Sciences and Engineering Research Council of Canada; Cystic Fibrosis Canada; Canadian Institutes of Health Research; Genome Canada; University of Toronto; Cystic Fibrosis Foundation","keywords":"Rope; Bayes' theorem; Parametric statistics; Sample size determination; Computer science; Statistical hypothesis testing; Nonparametric statistics; Likelihood-ratio test; Sample (material); Statistics; Biology; Computational biology; Mathematics; Bayesian probability; Artificial intelligence; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008467522,0.001421641,0.002261213,0.001858751,0.0006973725,0.00207357,0.003016053,0.001780237,0.005903324],"category_scores_gemma":[0.02535865,0.0009432893,0.003056773,0.001710487,0.001224018,0.001335188,0.00258,0.004943166,0.003193469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008638123,"about_ca_system_score_gemma":0.002221091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001364136,"about_ca_topic_score_gemma":0.00205847,"domain_scores_codex":[0.9957777,0.001909231,0.0002258014,0.0009159899,0.001022665,0.0001485565],"domain_scores_gemma":[0.9913042,0.006439659,0.0005909952,0.0008740801,0.0006178653,0.0001733301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001276794,0.0004906162,0.009496174,0.002063064,0.002414604,0.0009125573,0.0005534578,0.2060996,0.08097762,0.07499369,0.03094535,0.5897763],"study_design_scores_gemma":[0.0001813112,0.0002317601,0.003044283,0.00007574424,0.0001608246,0.0004665179,0.00007090618,0.8809267,0.02205499,0.0617775,0.03084693,0.0001625585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001444309,0.0001237445,0.9944554,0.00006888225,0.00003700225,0.00007545803,0.0006136856,0.002931267,0.0002501827],"genre_scores_gemma":[0.04825112,0.0002779747,0.941362,0.0003439903,0.00009316501,0.001311092,0.003227091,0.003271345,0.00186221],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008467522,"threshold_uncertainty_score":0.04478109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05012541260585335,"score_gpt":0.3522792142502935,"score_spread":0.3021538016444401,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}